Automatically determining data sets for a three-stage predictor

ABSTRACT

Data sets for a three-stage predictor can be automatically determined. For example, multiple time series can be filtered to identify a subset of time series that have time durations that exceed a preset time duration. Whether a time series of the subset of time series includes a time period with inactivity can be determined. Whether the time series exhibits a repetitive characteristic can be determined based on whether the time series has a pattern that repeats over a predetermined time period. Whether the time series includes a magnitude spike with a value above a preset magnitude can be determined. If the time series (i) lacks the time period with inactivity, (ii) exhibits the repetitive characteristic, and (iii) has the magnitude spike with the value above the preset magnitude threshold, the time series can be included in a data set for use with the three-stage predictor.

REFERENCE TO RELATED APPLICATION

This is a continuation of and claims the benefit of priority under 35U.S.C. §120 to U.S. patent application Ser. No. 15/233,400, titled“Three-Stage Predictor for Time Series” and filed on Aug. 10, 2016,which claims the benefit of priority under 35 U.S.C. §119(e) to U.S.Provisional Patent Application No. 62/212,542, titled “Three StageForecasting” and filed Aug. 31, 2015, to U.S. Provisional PatentApplication No. 62/219,191, titled “THREE STAGE FORECASTING” and filedSep. 16, 2015, and to U.S. Provisional Patent Application No.62/279,443, titled “Three Stage Forecasting” and filed Jan. 15, 2016,the entirety of each of which is hereby incorporated by referenceherein.

TECHNICAL FIELD

The present disclosure relates generally to modeling and simulation.More specifically, but not by way of limitation, this disclosure relatesto automatically determining data sets for a three-stage predictor.

BACKGROUND

Time series data can indicate interest in an object (e.g., a product)over a period of time. For example, time series data can include aseries of data points arranged in a sequential order over the period oftime with magnitudes representing the demand for the object over theperiod of time. It can be desirable to analyze the time series data topredict future interest in the object. But time series data can benoisy, span a short duration, and have other characteristics that canmake it challenging to analyze.

SUMMARY

In one example, a non-transitory computer readable medium comprisingprogram code that is executable by a processor is provided. The programcode can cause the processor to receive a plurality of time series. Eachtime series of the plurality of time series can comprise a plurality ofdata points arranged in a sequential order over a period of time. Theprogram code can cause the processor to filter the plurality of timeseries using a preset time duration to identify a subset of time seriesthat have time durations that exceed the preset time duration. Thepreset time duration can be a minimum time duration usable with apreselected forecasting process. The program code can cause theprocessor to determine that a time series of the subset of time seriesdoes not include a time period with inactivity. The program code cancause the processor to determine that the time series exhibits arepetitive characteristic based on the time series comprising a patternthat repeats over a predetermined time period. The program code cancause the processor to determine that the time series comprises amagnitude spike with a value above a preset magnitude threshold. Theprogram code can cause the processor to, in response to determining thatthe time series (i) lacks the time period with inactivity, (ii) exhibitsthe repetitive characteristic, and (iii) comprises the magnitude spikewith the value above the preset magnitude threshold: generate a data setthat includes the time series, and generate a predictive forecast fromthe data set using the preselected forecasting process. The predictiveforecast can indicate a progression of the time series over a futureperiod of time.

In another example, a method is provided that can include receiving aplurality of time series. Each time series of the plurality of timeseries can comprise a plurality of data points arranged in a sequentialorder over a period of time. The method can include filtering theplurality of time series using a preset time duration to identify asubset of time series that have time durations that exceed the presettime duration. The preset time duration can be a minimum time durationusable with a preselected forecasting process. The method can includedetermining that a time series of the subset of time series does notinclude a time period with inactivity. The method can includedetermining that the time series exhibits a repetitive characteristicbased on the time series comprising a pattern that repeats over apredetermined time period. The method can include determining that thetime series comprises a magnitude spike with a value above a presetmagnitude threshold. The method can include, in response to determiningthat the time series (i) lacks the time period with inactivity, (ii)exhibits the repetitive characteristic, and (iii) comprises themagnitude spike with the value above the preset magnitude threshold:generating a data set that includes the time series, and generating apredictive forecast from the data set using the preselected forecastingprocess. The predictive forecast can indicate a progression of the timeseries over a future period of time.

In another example, a system is provided that can include a processingdevice and a memory device. The memory device can include instructionsexecutable by the processing device for causing the processing device toreceive a plurality of time series. Each time series of the plurality oftime series can comprise a plurality of data points arranged in asequential order over a period of time. The instructions can cause theprocessing device to filter the plurality of time series using a presettime duration to identify a subset of time series that have timedurations that exceed the preset time duration. The preset time durationcan be a minimum time duration usable with a preselected forecastingprocess. The instructions can cause the processing device to determinethat a time series of the subset of time series does not include a timeperiod with inactivity. The instructions can cause the processing deviceto determine that the time series exhibits a repetitive characteristicbased on the time series comprising a pattern that repeats over apredetermined time period. The instructions can cause the processingdevice to determine that the time series comprises a magnitude spikewith a value above a preset magnitude threshold. The instructions cancause the processing device to, in response to determining that the timeseries (i) lacks the time period with inactivity, (ii) exhibits therepetitive characteristic, and (iii) comprises the magnitude spike withthe value above the preset magnitude threshold: generate a data set thatincludes the time series, and generate a predictive forecast from thedata set using the preselected forecasting process. The predictiveforecast can indicate a progression of the time series over a futureperiod of time.

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used in isolationto determine the scope of the claimed subject matter. The subject mattershould be understood by reference to appropriate portions of the entirespecification, any or all drawings, and each claim.

The foregoing, together with other features and examples, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appendedfigures:

FIG. 1 is a block diagram of an example of the hardware components of acomputing system according to some aspects.

FIG. 2 is an example of devices that can communicate with each otherover an exchange system and via a network according to some aspects.

FIG. 3 is a block diagram of a model of an example of a communicationsprotocol system according to some aspects.

FIG. 4 is a hierarchical diagram of an example of a communications gridcomputing system including a variety of control and worker nodesaccording to some aspects.

FIG. 5 is a flow chart of an example of a process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects.

FIG. 6 is a block diagram of a portion of a communications gridcomputing system including a control node and a worker node according tosome aspects.

FIG. 7 is a flow chart of an example of a process for executing a dataanalysis or processing project according to some aspects.

FIG. 8 is a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects.

FIG. 9 is a flow chart of an example of a process including operationsperformed by an event stream processing engine according to someaspects.

FIG. 10 is a block diagram of an ESP system interfacing between apublishing device and multiple event subscribing devices according tosome aspects.

FIG. 11 is a flow chart of an example of a process for determining if atime series is compatible with a particular process for predictingfuture interest in an object according to some aspects.

FIG. 12 is a flow chart of an example of a process for determining if amagnitude spike in a time series exceeds a magnitude threshold accordingto some aspects.

FIG. 13 is a flow chart of an example of a process for including a timeseries in a time series group according to some aspects.

FIG. 14 is a flow chart of an example of a process for predicting futureinterest in an object according to some aspects.

FIG. 15 is a flow chart of an example of a process for processing a timeseries according to some aspects.

FIG. 16 is a flow chart of an example of a process for determining aneffect of a moving event according to some aspects.

FIG. 17 is a graph of an example of time series data according to someaspects.

FIG. 18 is a graph of an example a decomposition of the time series fromFIG. 17 after smoothing the time series according to some aspects.

FIG. 19 is a graph of an example of the time series from FIG. 17 againsta seasonally adjusted time series according to some aspects.

FIG. 20 is a graph of an example of the time series from FIG. 17 againstthe estimated effect of promotions on the time series according to someaspects.

FIG. 21 is a graph of an example of the time series from FIG. 17 againsta residual time series after the effects of the promotions have beenremoved according to some aspects.

FIG. 22 is a graph of an example of a final forecast according to someaspects.

FIG. 23 is a graph of an example of actual sales against predicted salesgenerated using the three-stage process according to some aspects.

FIG. 24 is a graph of an example of actual sales against predicted salesgenerated using an exponential smoothing model according to someaspects.

FIG. 25 is a graph of an example of actual sales against predicted salesgenerated using an autoregressive integrated moving average modelaccording to some aspects.

In the appended figures, similar components or features can have thesame reference label. Further, various components of the same type canbe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofexamples of the technology. But various examples can be practicedwithout these specific details. The figures and description are notintended to be restrictive.

The ensuing description provides examples only, and is not intended tolimit the scope, applicability, or configuration of the disclosure.Rather, the ensuing description of the examples provides those skilledin the art with an enabling description for implementing an example.Various changes may be made in the function and arrangement of elementswithout departing from the spirit and scope of the technology as setforth in the appended claims.

Specific details are given in the following description to provide athorough understanding of the examples. But the examples may bepracticed without these specific details. For example, circuits,systems, networks, processes, and other components can be shown ascomponents in block diagram form to prevent obscuring the examples inunnecessary detail. In other examples, well-known circuits, processes,algorithms, structures, and techniques may be shown without unnecessarydetail in order to avoid obscuring the examples.

Also, individual examples can be described as a process that is depictedas a flowchart, a flow diagram, a data flow diagram, a structurediagram, or a block diagram. Although a flowchart can describe theoperations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations can be re-arranged. A process is terminated when itsoperations are completed, but can have additional operations notincluded in a figure. A process can correspond to a method, a function,a procedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination can correspond to a return of thefunction to the calling function or the main function.

Systems depicted in some of the figures can be provided in variousconfigurations. In some examples, the systems can be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing system.

Certain aspects and features of the present disclosure relate to using atime series associated with an object (e.g., a product) to predictfuture interest in the object over a future period of time. A computingdevice can generate predict the future interest using a three-stageprocess. For example, the computing device can implement a first stageof the three-stage process by identifying and removing a seasonalcomponent from the time series. The computing device can implement asecond stage of the three-stage process by identifying a moving event,such as Father's Day, associated with the time series and removing theeffects of the moving event from the time series. After the secondstage, the time series can be free from the seasonality and the effectsof the moving event. This version of the time series can be referred toas a residual time series. The computing device can implement a thirdstage of the three-stage process by using the residual, the seasonalaspect of the time series, and the effects of the moving event togenerate the prediction.

Not all time series may be compatible with the three-stage process. Acomputing device according to some examples can determine if the timeseries is compatible with the three-stage process prior to implementingthe three-stage process using the time series. If so, the computingdevice can implement the three-stage process to predict future interestin the object. If not, the computing device can implement anotherprocess to predict future interest in the object.

Some examples can reduce the total number of processing iterations,time, memory, and electrical power used by the computing device topredict the future interest in the object. For example, the three-stageprocess can require fewer computational iterations and less memory usagethan other predictive processes, while obtaining a similar level ofaccuracy. Thus, the three-stage process can be faster andcomputationally less expensive than other predictive processes. Further,in some examples, the computing device can select an appropriatepredictive process to use with a particular time series, which canensure that unnecessary computational iterations are not performed andmemory space is wasted by attempting to use an incompatible predictiveprocess with the particular time series.

FIGS. 1-10 depict examples of systems and methods usable for athree-stage predictor according to some aspects. For example, FIG. 1 isa block diagram of an example of the hardware components of a computingsystem according to some aspects. Data transmission network 100 is aspecialized computer system that may be used for processing largeamounts of data where a large number of computer processing cycles arerequired.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. The computing environment 114 may include one or more othersystems. For example, computing environment 114 may include a databasesystem 118 or a communications grid 120.

Data transmission network 100 also includes one or more network devices102. Network devices 102 may include client devices that can communicatewith computing environment 114. For example, network devices 102 maysend data to the computing environment 114 to be processed, may sendcommunications to the computing environment 114 to control differentaspects of the computing environment or the data it is processing, amongother reasons. Network devices 102 may interact with the computingenvironment 114 through a number of ways, such as, for example, over oneor more networks 108.

In some examples, network devices 102 may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP)), to the computing environment 114 vianetworks 108. For example, the network devices 102 can transmitelectronic messages for use in predicting future interest in an objectfrom time series data associated with the object, all at once orstreaming over a period of time, to the computing environment 114 vianetworks 108.

The network devices 102 may include network computers, sensors,databases, or other devices that may transmit or otherwise provide datato computing environment 114. For example, network devices 102 mayinclude local area network devices, such as routers, hubs, switches, orother computer networking devices. These devices may provide a varietyof stored or generated data, such as network data or data specific tothe network devices 102 themselves. Network devices 102 may also includesensors that monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devices102 may provide data they collect over time. Network devices 102 mayalso include devices within the internet of things, such as deviceswithin a home automation network. Some of these devices may be referredto as edge devices, and may involve edge-computing circuitry. Data maybe transmitted by network devices 102 directly to computing environment114 or to network-attached data stores, such as network-attached datastores 110 for storage so that the data may be retrieved later by thecomputing environment 114 or other portions of data transmission network100. For example, the network devices 102 can transmit data usable forpredicting future interest in an object to a network-attached data store110 for storage. The computing environment 114 may later retrieve thedata from the network-attached data store 110 and use the data topredict future interest in the object.

Network-attached data stores 110 can store data to be processed by thecomputing environment 114 as well as any intermediate or final datagenerated by the computing system in non-volatile memory. But in certainexamples, the configuration of the computing environment 114 allows itsoperations to be performed such that intermediate and final data resultscan be stored solely in volatile memory (e.g., RAM), without arequirement that intermediate or final data results be stored tonon-volatile types of memory (e.g., disk). This can be useful in certainsituations, such as when the computing environment 114 receives ad hocqueries from a user and when responses, which are generated byprocessing large amounts of data, need to be generated dynamically(e.g., on the fly). In this situation, the computing environment 114 maybe configured to retain the processed information within memory so thatresponses can be generated for the user at different levels of detail aswell as allow a user to interactively query against this information.

Network-attached data stores 110 may store a variety of different typesof data organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data stores may includestorage other than primary storage located within computing environment114 that is directly accessible by processors located therein.Network-attached data stores may include secondary, tertiary orauxiliary storage, such as large hard drives, servers, virtual memory,among other types. Storage devices may include portable or non-portablestorage devices, optical storage devices, and various other mediumscapable of storing, containing data. A machine-readable storage mediumor computer-readable storage medium may include a non-transitory mediumin which data can be stored and that does not include carrier waves ortransitory electronic communications. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode or machine-executable instructions that may represent a procedure,a function, a subprogram, a program, a routine, a subroutine, a module,a software package, a class, or any combination of instructions, datastructures, or program statements. A code segment may be coupled toanother code segment or a hardware circuit by passing or receivinginformation, data, arguments, parameters, or memory contents.Information, arguments, parameters, data, etc. may be passed, forwarded,or transmitted via any suitable means including memory sharing, messagepassing, token passing, network transmission, among others. Furthermore,the data stores may hold a variety of different types of data. Forexample, network-attached data stores 110 may hold unstructured (e.g.,raw) data.

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time-stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data or structured hierarchically according to oneor more dimensions (e.g., parameters, attributes, or variables). Forexample, data may be stored in a hierarchical data structure, such as arelational online analytical processing (ROLAP) or multidimensionalonline analytical processing (MOLAP) database, or may be stored inanother tabular form, such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the sever farms 106 or one or more servers within the server farms106. Server farms 106 can be configured to provide information in apredetermined manner. For example, server farms 106 may access data totransmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or morewebsites, sensors, as inputs from a control database, or may have beenreceived as inputs from an external system or device. Server farms 106may assist in processing the data by turning raw data into processeddata based on one or more rules implemented by the server farms. Forexample, sensor data may be analyzed to determine changes in anenvironment over time or in real-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain examples, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system ondemand. Cloud network 116 is shown in FIG. 1 as being connected tocomputing environment 114 (and therefore having computing environment114 as its client or user), but cloud network 116 may be connected to orutilized by any of the devices in FIG. 1. Services provided by the cloudnetwork 116 can dynamically scale to meet the needs of its users. Thecloud network 116 may include one or more computers, servers, orsystems. In some examples, the computers, servers, or systems that makeup the cloud network 116 are different from the user's own on-premisescomputers, servers, or systems. For example, the cloud network 116 mayhost an application, and a user may, via a communication network such asthe Internet, order and use the application on demand. In some examples,the cloud network 116 may host an application for performing dataanalytics or predicting future interest in an object.

While each device, server, and system in FIG. 1 is shown as a singledevice, multiple devices may instead be used. For example, a set ofnetwork devices can be used to transmit various communications from asingle user, or remote server 140 may include a server stack. As anotherexample, data may be processed as part of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between a device and connection management system 150,between server farms 106 and computing environment 114, or between aserver and a device) may occur over one or more networks 108. Networks108 may include one or more of a variety of different types of networks,including a wireless network, a wired network, or a combination of awired and wireless network. Examples of suitable networks include theInternet, a personal area network, a local area network (LAN), a widearea network (WAN), or a wireless local area network (WLAN). A wirelessnetwork may include a wireless interface or combination of wirelessinterfaces. As an example, a network in the one or more networks 108 mayinclude a short-range communication channel, such as a Bluetooth or aBluetooth Low Energy channel. A wired network may include a wiredinterface. The wired or wireless networks may be implemented usingrouters, access points, bridges, gateways, or the like, to connectdevices in the network 108. The networks 108 can be incorporatedentirely within or can include an intranet, an extranet, or acombination thereof. In one example, communications between two or moresystems or devices can be achieved by a secure communications protocol,such as secure sockets layer (SSL) or transport layer security (TLS). Inaddition, data or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics.

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The computing nodes in the communicationsgrid 120 and the transmission network database system 118 may share thesame processor hardware, such as processors that are located withincomputing environment 114.

In some examples, the computing environment 114, a network device 102,or both can implement one or more processes for predicting futureinterest in an object. For example, the computing environment 114, anetwork device 102, or both can implement one or more versions of theprocesses discussed with respect to FIGS. 11-12.

FIG. 2 is an example of devices that can communicate with each otherover an exchange system and via a network according to some aspects. Asnoted, each communication within data transmission network 100 may occurover one or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2, network device 204 can transmit a communication overa network (e.g., a cellular network via a base station 210). In someexamples, the communication can include times series data. Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. In someexamples, the network device 204 may collect data either from itssurrounding environment or from other network devices (such as networkdevices 205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, and electrical current, amongothers. The sensors may be mounted to various components used as part ofa variety of different types of systems. The network devices may detectand record data related to the environment that it monitors, andtransmit that data to computing environment 214.

The network devices 204-209 may also perform processing on data itcollects before transmitting the data to the computing environment 214,or before deciding whether to transmit data to the computing environment214. For example, network devices 204-209 may determine whether datacollected meets certain rules, for example by comparing data or valuescalculated from the data and comparing that data to one or morethresholds. The network devices 204-209 may use this data or comparisonsto determine if the data is to be transmitted to the computingenvironment 214 for further use or processing. In some examples, thenetwork devices 204-209 can pre-process the data prior to transmittingthe data to the computing environment 214. For example, the networkdevices 204-209 can reformat the data before transmitting the data tothe computing environment 214 for further processing (e.g., analyzingthe data to predicting future interest in an object associated with thedata).

Computing environment 214 may include machines 220, 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines 220,240, computing environment 214 may have only one machine or may havemore than two machines. The machines 220, 240 that make up computingenvironment 214 may include specialized computers, servers, or othermachines that are configured to individually or collectively processlarge amounts of data. The computing environment 214 may also includestorage devices that include one or more databases of structured data,such as data organized in one or more hierarchies, or unstructured data.The databases may communicate with the processing devices withincomputing environment 214 to distribute data to them. Since networkdevices may transmit data to computing environment 214, that data may bereceived by the computing environment 214 and subsequently stored withinthose storage devices. Data used by computing environment 214 may alsobe stored in data stores 235, which may also be a part of or connectedto computing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withclient devices 230 via one or more routers 225. Computing environment214 may collect, analyze or store data from or pertaining tocommunications, client device operations, client rules, oruser-associated actions stored at one or more data stores 235. Such datamay influence communication routing to the devices within computingenvironment 214, how data is stored or processed within computingenvironment 214, among other actions.

Notably, various other devices can further be used to influencecommunication routing or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2, computing environment 214 may include amachine 240 that is a web server. Computing environment 214 can retrievedata of interest, such as client information (e.g., product information,client rules, etc.), technical product details, news, blog posts,e-mails, forum posts, electronic documents, social media posts (e.g.,Twitter™ posts or Facebook™ posts), and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices 204-209 may receive data periodically and in real time from aweb server or other source. Devices within computing environment 214 mayalso perform pre-analysis on data it receives to determine if the datareceived should be processed as part of an ongoing project. For example,as part of a project in which future interest in an object is determinedfrom data, the computing environment 214 can perform a pre-analysis ofthe data. The pre-analysis can include determining whether the data isin a correct format for predicting future interest in an object usingthe data and, if not, reformatting the data into the correct format.

FIG. 3 is a block diagram of a model of an example of a communicationsprotocol system according to some aspects. More specifically, FIG. 3identifies operation of a computing environment in an Open SystemsInteraction model that corresponds to various connection components. Themodel 300 shows, for example, how a computing environment, such ascomputing environment (or computing environment 214 in FIG. 2) maycommunicate with other devices in its network, and control howcommunications between the computing environment and other devices areexecuted and under what conditions.

The model 300 can include layers 302-314. The layers 302-314 arearranged in a stack. Each layer in the stack serves the layer one levelhigher than it (except for the application layer, which is the highestlayer), and is served by the layer one level below it (except for thephysical layer 302, which is the lowest layer). The physical layer 302is the lowest layer because it receives and transmits raw bites of data,and is the farthest layer from the user in a communications system. Onthe other hand, the application layer is the highest layer because itinteracts directly with a software application.

As noted, the model 300 includes a physical layer 302. Physical layer302 represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagneticcommunications. Physical layer 302 also defines protocols that maycontrol communications within a data transmission network.

Link layer 304 defines links and mechanisms used to transmit (e.g.,move) data across a network. The link layer manages node-to-nodecommunications, such as within a grid-computing environment. Link layer304 can detect and correct errors (e.g., transmission errors in thephysical layer 302). Link layer 304 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 306 can define the protocol for routing within a network.In other words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid-computing environment).Network layer 306 can also define the processes used to structure localaddressing within the network.

Transport layer 308 can manage the transmission of data and the qualityof the transmission or receipt of that data. Transport layer 308 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 308 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 310 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 312 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt or format data based on data types known to beaccepted by an application or network layer.

Application layer 314 interacts directly with software applications andend users, and manages communications between them. Application layer314 can identify destinations, local resource states or availability orcommunication content or formatting using the applications.

For example, a communication link can be established between two deviceson a network. One device can transmit an analog or digitalrepresentation of an electronic message that includes a data set to theother device. The other device can receive the analog or digitalrepresentation at the physical layer 302. The other device can transmitthe data associated with the electronic message through the remaininglayers 304-314. The application layer 314 can receive data associatedwith the electronic message. The application layer 314 can identify oneor more applications, such as a forecasting application, to which totransmit data associated with the electronic message. The applicationlayer 314 can transmit the data to the identified application.

Intra-network connection components 322, 324 can operate in lowerlevels, such as physical layer 302 and link layer 304, respectively. Forexample, a hub can operate in the physical layer, a switch can operatein the physical layer, and a router can operate in the network layer.Inter-network connection components 326, 328 are shown to operate onhigher levels, such as layers 306-314. For example, routers can operatein the network layer and network devices can operate in the transport,session, presentation, and application layers.

A computing environment 330 can interact with or operate on, in variousexamples, one, more, all or any of the various layers. For example,computing environment 330 can interact with a hub (e.g., via the linklayer) to adjust which devices the hub communicates with. The physicallayer 302 may be served by the link layer 304, so it may implement suchdata from the link layer 304. For example, the computing environment 330may control which devices from which it can receive data. For example,if the computing environment 330 knows that a certain network device hasturned off, broken, or otherwise become unavailable or unreliable, thecomputing environment 330 may instruct the hub to prevent any data frombeing transmitted to the computing environment 330 from that networkdevice. Such a process may be beneficial to avoid receiving data that isinaccurate or that has been influenced by an uncontrolled environment.As another example, computing environment 330 can communicate with abridge, switch, router or gateway and influence which device within thesystem (e.g., system 200) the component selects as a destination. Insome examples, computing environment 330 can interact with variouslayers by exchanging communications with equipment operating on aparticular layer by routing or modifying existing communications. Inanother example, such as in a grid-computing environment, a node maydetermine how data within the environment should be routed (e.g., whichnode should receive certain data) based on certain parameters orinformation provided by other layers within the model.

The computing environment 330 may be a part of a communications gridenvironment, the communications of which may be implemented as shown inthe protocol of FIG. 3. For example, referring back to FIG. 2, one ormore of machines 220 and 240 may be part of a communicationsgrid-computing environment. A gridded computing environment may beemployed in a distributed system with non-interactive workloads wheredata resides in memory on the machines, or compute nodes. In such anenvironment, analytic code, instead of a database management system, cancontrol the processing performed by the nodes. Data is co-located bypre-distributing it to the grid nodes, and the analytic code on eachnode loads the local data into memory. Each node may be assigned aparticular task, such as a portion of a processing project, or toorganize or control other nodes within the grid. For example, each nodemay be assigned a portion of a processing task for predicting futureinterest in an object.

FIG. 4 is a hierarchical diagram of an example of a communications gridcomputing system 400 including a variety of control and worker nodesaccording to some aspects. Communications grid computing system 400includes three control nodes and one or more worker nodes.Communications grid computing system 400 includes control nodes 402,404, and 406. The control nodes are communicatively connected viacommunication paths 451, 453, and 455. The control nodes 402-406 maytransmit information (e.g., related to the communications grid ornotifications) to and receive information from each other. Althoughcommunications grid computing system 400 is shown in FIG. 4 as includingthree control nodes, the communications grid may include more or lessthan three control nodes.

Communications grid computing system 400 (which can be referred to as a“communications grid”) also includes one or more worker nodes. Shown inFIG. 4 are six worker nodes 410-420. Although FIG. 4 shows six workernodes, a communications grid can include more or less than six workernodes. The number of worker nodes included in a communications grid maybe dependent upon how large the project or data set is being processedby the communications grid, the capacity of each worker node, the timedesignated for the communications grid to complete the project, amongothers. Each worker node within the communications grid computing system400 may be connected (wired or wirelessly, and directly or indirectly)to control nodes 402-406. Each worker node may receive information fromthe control nodes (e.g., an instruction to perform work on a project)and may transmit information to the control nodes (e.g., a result fromwork performed on a project). Furthermore, worker nodes may communicatewith each other directly or indirectly. For example, worker nodes maytransmit data between each other related to a forecasting job beingperformed or an individual task within a forecasting being performed bythat worker node. In some examples, worker nodes may not be connected(communicatively or otherwise) to certain other worker nodes. Forexample, a worker node 410 may only be able to communicate with aparticular control node 402. The worker node 410 may be unable tocommunicate with other worker nodes 412-420 in the communications grid,even if the other worker nodes 412-420 are controlled by the samecontrol node 402.

A control node 402-406 may connect with an external device with whichthe control node 402-406 may communicate (e.g., a communications griduser, such as a server or computer, may connect to a controller of thegrid). For example, a server or computer may connect to control nodes402-406 and may transmit a project or job to the node, such as a projector job related to predicting future interest in an object from a dataset. The project may include the data set. The data set may be of anysize and can include a time series. Once the control node 402-406receives such a project including a large data set, the control node maydistribute the data set or projects related to the data set to beperformed by worker nodes. Alternatively, for a project including alarge data set, the data set may be receive or stored by a machine otherthan a control node 402-406 (e.g., a Hadoop data node).

Control nodes 402-406 can maintain knowledge of the status of the nodesin the grid (e.g., grid status information), accept work requests fromclients, subdivide the work across worker nodes, and coordinate theworker nodes, among other responsibilities. Worker nodes 412-420 mayaccept work requests from a control node 402-406 and provide the controlnode with results of the work performed by the worker node. A grid maybe started from a single node (e.g., a machine, computer, server, etc.).This first node may be assigned or may start as the primary control node402 that will control any additional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (e.g., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project code running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node 402 receivesa project, the primary control node may distribute portions of theproject to its worker nodes for execution. For example, a project forpredicting future interest in an object can be initiated oncommunications grid computing system 400. A primary control node cancontrol the work to be performed for the project in order to completethe project as requested or instructed. The primary control node maydistribute work to the worker nodes 412-420 based on various factors,such as which subsets or portions of projects may be completed mostefficiently and in the correct amount of time. For example, a workernode 412 may predict future interest in an object using at least aportion of data that is already local (e.g., stored on) the worker node.The primary control node also coordinates and processes the results ofthe work performed by each worker node 412-420 after each worker node412-420 executes and completes its job. For example, the primary controlnode may receive a result from one or more worker nodes 412-420, and theprimary control node may organize (e.g., collect and assemble) theresults received and compile them to produce a complete result for theproject received from the end user.

Any remaining control nodes, such as control nodes 404, 406, may beassigned as backup control nodes for the project. In an example, backupcontrol nodes may not control any portion of the project. Instead,backup control nodes may serve as a backup for the primary control nodeand take over as primary control node if the primary control node wereto fail. If a communications grid were to include only a single controlnode 402, and the control node 402 were to fail (e.g., the control nodeis shut off or breaks) then the communications grid as a whole may failand any project or job being run on the communications grid may fail andmay not complete. While the project may be run again, such a failure maycause a delay (severe delay in some cases, such as overnight delay) incompletion of the project. Therefore, a grid with multiple control nodes402-406, including a backup control node, may be beneficial.

In some examples, the primary control node may open a pair of listeningsockets to add another node or machine to the grid. A socket may be usedto accept work requests from clients, and the second socket may be usedto accept connections from other grid nodes. The primary control nodemay be provided with a list of other nodes (e.g., other machines,computers, servers, etc.) that can participate in the grid, and the rolethat each node can fill in the grid. Upon startup of the primary controlnode (e.g., the first node on the grid), the primary control node mayuse a network protocol to start the server process on every other nodein the grid. Command line parameters, for example, may inform each nodeof one or more pieces of information, such as: the role that the nodewill have in the grid, the host name of the primary control node, theport number on which the primary control node is accepting connectionsfrom peer nodes, among others. The information may also be provided in aconfiguration file, transmitted over a secure shell tunnel, recoveredfrom a configuration server, among others. While the other machines inthe grid may not initially know about the configuration of the grid,that information may also be sent to each other node by the primarycontrol node. Updates of the grid information may also be subsequentlysent to those nodes.

For any control node other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it can check to see if it alreadyhas a connection to that other node. If it does not have a connection tothat node, it may then establish a connection to that control node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. But, a hierarchy of nodes may also be determinedusing methods other than using the unique identifiers of the nodes. Forexample, the hierarchy may be predetermined, or may be assigned based onother predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404, 406 (and, for example, toother control or worker nodes 412-420 within the communications grid).Such communications may be sent periodically, at fixed time intervals,between known fixed stages of the project's execution, among otherprotocols. The communications transmitted by primary control node 402may be of varied types and may include a variety of types ofinformation. For example, primary control node 402 may transmitsnapshots (e.g., status information) of the communications grid so thatbackup control node 404 always has a recent snapshot of thecommunications grid. The snapshot or grid status may include, forexample, the structure of the grid (including, for example, the workernodes 410-420 in the communications grid, unique identifiers of theworker nodes 410-420, or their relationships with the primary controlnode 402) and the status of a project (including, for example, thestatus of each worker node's portion of the project). The snapshot mayalso include analysis or results received from worker nodes 410-420 inthe communications grid. The backup control nodes 404, 406 may receiveand store the backup data received from the primary control node 402.The backup control nodes 404, 406 may transmit a request for such asnapshot (or other information) from the primary control node 402, orthe primary control node 402 may send such information periodically tothe backup control nodes 404, 406.

As noted, the backup data may allow a backup control node 404, 406 totake over as primary control node if the primary control node 402 failswithout requiring the communications grid to start the project over fromscratch. If the primary control node 402 fails, the backup control node404, 406 that will take over as primary control node may retrieve themost recent version of the snapshot received from the primary controlnode 402 and use the snapshot to continue the project from the stage ofthe project indicated by the backup data. This may prevent failure ofthe project as a whole.

A backup control node 404, 406 may use various methods to determine thatthe primary control node 402 has failed. In one example of such amethod, the primary control node 402 may transmit (e.g., periodically) acommunication to the backup control node 404, 406 that indicates thatthe primary control node 402 is working and has not failed, such as aheartbeat communication. The backup control node 404, 406 may determinethat the primary control node 402 has failed if the backup control nodehas not received a heartbeat communication for a certain predeterminedperiod of time. Alternatively, a backup control node 404, 406 may alsoreceive a communication from the primary control node 402 itself (beforeit failed) or from a worker node 410-420 that the primary control node402 has failed, for example because the primary control node 402 hasfailed to communicate with the worker node 410-420.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404,406) can take over for failed primary control node 402 and become thenew primary control node. For example, the new primary control node maybe chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative example, a backupcontrol node may be assigned to be the new primary control node byanother device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeexample, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeexample, the primary control node may transmit a communication to eachof the operable worker nodes still on the communications grid that eachof the worker nodes should purposefully fail also. After each of theworker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed. In someexamples, a communications grid computing system 400 can be used topredict future interest in an object.

FIG. 5 is a flow chart of an example of a process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects. The process may include,for example, receiving grid status information including a projectstatus of a portion of a project being executed by a node in thecommunications grid, as described in operation 502. For example, acontrol node (e.g., a backup control node connected to a primary controlnode and a worker node on a communications grid) may receive grid statusinformation, where the grid status information includes a project statusof the primary control node or a project status of the worker node. Theproject status of the primary control node and the project status of theworker node may include a status of one or more portions of a projectbeing executed by the primary and worker nodes in the communicationsgrid. The process may also include storing the grid status information,as described in operation 504. For example, a control node (e.g., abackup control node) may store the received grid status informationlocally within the control node. Alternatively, the grid statusinformation may be sent to another device for storage where the controlnode may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 is a block diagram of a portion of a communications gridcomputing system 600 including a control node and a worker nodeaccording to some aspects. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viacommunication path 650.

Similar to in FIG. 4, communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 comprise multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes a database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG.2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However in certain examples, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DMBS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 610 may be connected via a network,such as network 108 shown in FIG. 1. Therefore, nodes 602 and 610 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.Each node may be capable of operating as either a control node or aworker node. The GESC at the control node 602 can communicate, over acommunication path 652, with a client device 630. More specifically,control node 602 may communicate with client application 632 hosted bythe client device 630 to receive queries and to respond to those queriesafter processing large amounts of data.

DMBS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within nodes 602 or 610. The database mayorganize data stored in data stores 624. The DMBS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4, data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 is a flow chart of an example of a process for executing a dataanalysis or a processing project according to some aspects. As describedwith respect to FIG. 6, the GESC at the control node may transmit datawith a client device (e.g., client device 630) to receive queries forexecuting a project and to respond to those queries after large amountsof data have been processed. The query may be transmitted to the controlnode, where the query may include a request for executing a project, asdescribed in operation 702. The query can contain instructions on thetype of data analysis to be performed in the project and whether theproject should be executed using the grid-based computing environment,as shown in operation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project.

As noted with respect to FIG. 2, the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2, and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 1024a-c, described further with respect to FIG. 10, may also subscribe tothe ESPE. The ESPE may determine or define how input data or eventstreams from network devices or other publishers (e.g., network devices204-209 in FIG. 2) are transformed into meaningful output data to beconsumed by subscribers, such as for example client devices 230 in FIG.2.

FIG. 8 is a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects. ESPE 800 may includeone or more projects 802. A project may be described as a second-levelcontainer in an engine model managed by ESPE 800 where a thread poolsize for the project may be defined by a user. Each project of the oneor more projects 802 may include one or more continuous queries 804 thatcontain data flows, which are data transformations of incoming eventstreams. The one or more continuous queries 804 may include one or moresource windows 806 and one or more derived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2. As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2.The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeexample, there may be only one ESPE 800 for each instance of the ESPapplication, and ESPE 800 may have a unique engine name. Additionally,the one or more projects 802 may each have unique project names, andeach query may have a unique continuous query name and begin with auniquely named source window of the one or more source windows 806. ESPE800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 is a flow chart of an example of a process including operationsperformed by an event stream processing engine according to someaspects. As noted, the ESPE 800 (or an associated ESP application)defines how input event streams are transformed into meaningful outputevent streams. More specifically, the ESP application may define howinput event streams from publishers (e.g., network devices providingsensed data) are transformed into meaningful output event streamsconsumed by subscribers (e.g., a data analytics project being executedby a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. Variousoperations may be performed in parallel, for example, using a pluralityof threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 is a block diagram of an ESP system 1000 interfacing betweenpublishing device 1022 and event subscribing devices 1024 a-c accordingto some aspects. ESP system 1000 may include ESP device or subsystem1001, publishing device 1022, an event subscribing device A 1024 a, anevent subscribing device B 1024 b, and an event subscribing device C1024 c. Input event streams are output to ESP device 1001 by publishingdevice 1022. In alternative embodiments, the input event streams may becreated by a plurality of publishing devices. The plurality ofpublishing devices further may publish event streams to other ESPdevices. The one or more continuous queries instantiated by ESPE 800 mayanalyze and process the input event streams to form output event streamsoutput to event subscribing device A 1024 a, event subscribing device B1024 b, and event subscribing device C 1024 c. ESP system 1000 mayinclude a greater or a fewer number of event subscribing devices ofevent subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 1022, to publish eventstreams into ESPE 800 or an event subscriber, such as event subscribingdevice A 1024 a, event subscribing device B 1024 b, and eventsubscribing device C 1024 c, to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may bedefined. Using the publish/subscribe API, an event publishingapplication may publish event streams into a running event streamprocessor project source window of ESPE 800, and the event subscriptionapplication may subscribe to an event stream processor project sourcewindow of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 1022, and event subscription applications instantiatedat one or more of event subscribing device A 1024 a, event subscribingdevice B 1024 b, and event subscribing device C 1024 c.

Referring back to FIG. 9, operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of thepublishing device 1022.

ESP subsystem 800 may include a publishing client 1002, ESPE 800, asubscribing client A 1004, a subscribing client B 1006, and asubscribing client C 1008. Publishing client 1002 may be started by anevent publishing application executing at publishing device 1022 usingthe publish/subscribe API. Subscribing client A 1004 may be started byan event subscription application A, executing at event subscribingdevice A 1024 a using the publish/subscribe API. Subscribing client B1006 may be started by an event subscription application B executing atevent subscribing device B 1024 b using the publish/subscribe API.Subscribing client C 1008 may be started by an event subscriptionapplication C executing at event subscribing device C 1024 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on publishing device 1022.The event block object may generated, for example, by the eventpublishing application and may be received by publishing client 1002. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 1004, subscribingclient B 806, and subscribing client C 808 and to event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c. Publishing client 1002 may furthergenerate and include a unique embedded transaction ID in the event blockobject as the event block object is processed by a continuous query, aswell as the unique ID that publishing device 1022 assigned to the eventblock object.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscribing devices 1024 a-c. For example, subscribing client A 804,subscribing client B 806, and subscribing client C 808 may send thereceived event block object to event subscription device A 1024 a, eventsubscription device B 1024 b, and event subscription device C 1024 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 1022,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some examples, big data is processed for an analyticsproject after the data is received and stored. In other examples,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2, data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the present disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations, suchas those in support of an ongoing manufacturing or drilling operation.An example of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, aprocessor and a computer-readable medium operably coupled to theprocessor. The processor is configured to execute an ESP engine (ESPE).The computer-readable medium has instructions stored thereon that, whenexecuted by the processor, cause the computing device to support thefailover. An event block object is received from the ESPE that includesa unique identifier. A first status of the computing device as active orstandby is determined. When the first status is active, a second statusof the computing device as newly active or not newly active isdetermined. Newly active is determined when the computing device isswitched from a standby status to an active status. When the secondstatus is newly active, a last published event block object identifierthat uniquely identifies a last published event block object isdetermined. A next event block object is selected from a non-transitorycomputer-readable medium accessible by the computing device. The nextevent block object has an event block object identifier that is greaterthan the determined last published event block object identifier. Theselected next event block object is published to an out-messagingnetwork device. When the second status of the computing device is notnewly active, the received event block object is published to theout-messaging network device. When the first status of the computingdevice is standby, the received event block object is stored in thenon-transitory computer-readable medium.

FIG. 11 is a flow chart of an example of a process for determining if atime series is compatible with a particular process for predictingfuture interest in an object according to some aspects. All time-seriesare not necessarily compatible with all processes for generatingpredictions of future interest (e.g., forecasts). A computing deviceaccording to some examples can determine whether a particular timeseries is compatible with a particular process for generating aprediction of future interest (e.g., prior to trying to use the timeseries with the particular process). In some examples, the computingdevice can include or use three stages to generate the prediction, asdiscussed with respect to FIG. 14.

Some examples can include more, fewer, or different steps than the stepsdepicted in FIG. 11. Also, some examples can implement the steps of theprocess in a different order. Some examples can be implemented using anyof the systems and processes described with respect to FIGS. 1-10.

In block 1102, a processor receives a time series. The time series canbe associated with an object (e.g., a product, such as a ticket; car;mobile phone; utility, such as electricity or water; a hotel room;etc.). The time series can represent interest in (e.g., demand for,sales of, or use of) the object. The time series can include a series ofdata points arranged in a sequential order over the period of time andhaving magnitudes that indicate the amount of interest in the objectover the period of time.

In some examples, the processor can receive the time series from a localmemory device. For example, the processor can retrieve the time seriesfrom a local memory device. In other examples, the processor can receivethe time series from a remote computing device via a network. Forexample, the processor can retrieve the time series from a remotedatabase via the Internet. The processor can receive some or all of thetime series from any number and combination of computing devices,databases, and memory devices.

In block 1104, the processor determines if the time series spans aduration that is greater than or equal to a first duration threshold,which can be preset or predetermined. An example of the first durationthreshold can be one year. For example, the processor can determine ifthe time series spans a duration that is greater than or equal to oneyear.

If the processor determines that the time series spans a duration thatis greater than or equal the first duration threshold, the process canproceed to block 1106. Otherwise, the processor can determine that thetime series is incompatible with a particular prediction process, suchas a three-stage process. In some examples, if the processor determinesthat the time series is incompatible with the particular predictionprocess, the process can proceed to block 1118. In block 1118, theprocessor can select a different predictive process for use with thetime series. Examples of the different predictive process can includeusing an autoregressive integrated moving average (ARIMA) model, anARIMAX model, or an exponential smoothing model (ESM). For example, theprocessor can determine that another predictive process can be used withtime series that spans a duration of less than one year. The processorcan select the other predictive process for use with the time series topredict interest in the object. In some examples, the processor canproceed to block 1120, in which the processor can predict interest inthe object using the time series as input for the other predictiveprocess.

In block 1106, the processor determines if the time series spans aduration that is greater than or equal to a second duration threshold,which can be preset or predetermined. An example of the second durationthreshold can be two years. For example, the processor can determine ifthe time series spans a duration that is greater than or equal to twoyears.

If the time series spans a duration that is greater than or equal to thesecond duration threshold, the time series can be referred to as a longtime series. If the time series spans a duration that is greater than orequal to the first duration threshold, but less than the second durationthreshold, the time series can be referred to as a short time series.

In block 1108, the processor determines if the time series includes aperiod of inactivity. The period of inactivity can include consecutivehours, days, weeks, or months of inactivity. For example, the processorcan analyze the time series to determine if the time series includes atimespan of a predetermined length (e.g., a predetermined number ofconsecutive hours, days, weeks, or months) that has magnitudes below apredetermined magnitude threshold. If so, the processor can determinethat the time series includes the period of inactivity. Such a timeseries can be referred to as a short time span (STS) series, and can beincompatible with the particular predictive process. Otherwise, theprocessor can determine that the time series does not include the periodof inactivity (e.g., the time series has a consistent and continuouspattern of activity). Such a time series can be referred to as a longtime span (LTS) series.

As a particular example, the time series can relate to interest in aproduct over a one year period. The processor can analyze the timeseries to determine if the time series includes a timespan (e.g., aconsecutive time period) that is at least a week long during whichdemand was below a predetermined amount, such as 50 units. If so, theprocessor can determine that the time series includes the period ofinactivity, and is a STS series. If not, the processor can determinethat the time series does not include the period of inactivity, and is aLTS series. If the processor determines that the time series does notinclude the period of inactivity (e.g., the time series is a LTSseries), the process can continue to block 1110.

In block 1110, the processor determines if at least a portion of thetime series is repetitive (e.g., seasonal or periodic). For example, theprocessor can analyze the magnitudes of the time series to determine ifa pattern of magnitudes exists that repeats at least once during theduration of the time series. In some examples, the processor candetermine that the time series includes a particular pattern ofmagnitudes occurring on a daily, weekly, monthly, quarterly, or yearlycycle, or with another frequency. If the processor determines that thetime series is not repetitive, the processor may determine that the timeseries is incompatible with the particular predictive process.Alternatively, if the processor determines that the time series is notrepetitive, the process can continue to block 1112. If the processordetermines that the time series is repetitive, the process can continueto block 1114.

In block 1112, the processor combines the time series with additionaltime-series data. The processor can receive the additional time-seriesdata from a local memory device or a remote computing device via anetwork. In some examples, the processor can combine the time serieswith the additional time-series data by appending the additionaltime-series data to, prepending the additional time-series data to, orotherwise including the additional time-series data in the time series.The processor can additionally or alternatively combine the time serieswith the additional time-series data using a hierarchical aggregationtechnique. The additional time-series data can include another timeseries, which can be associated with the same object or a differentobject. In some examples, combining the time series with the additionaltime-series data can extend the length of the time series enough for theprocessor to be able to determine a repetitive characteristic of thetime series.

In some examples, the processor can perform step 1112 prior to block1110. For example, a short time series may not have enough data for theprocessor to determine a repetitive characteristic of the short timeseries. So, if the processor determines that the time series is a shorttime series, the processor can combine the short time series withadditional time-series data to extend the length of the short timeseries prior to determining if the time series is repetitive.

In block 1114, the processor determines if the time series includes amagnitude spike that exceeds a magnitude threshold, which can be apreset or predetermined threshold. In some examples, the processor candetermine if the time series includes the magnitude spike that exceedsthe magnitude threshold by performing one or more steps shown in FIG.12.

Turning to FIG. 12, in block 1202, the processor generates a base timeseries by removing magnitude spikes in the time series that are relatedto moving events. A moving event can include an event that occurs ondifferent days for two consecutive years. Examples of moving events canbe Easter, Mother's Day, Father's Day, a promotion, a company event,etc. A moving event can be represented in the time series as a magnitudespike or pattern of magnitude spikes that occurs on different daysduring two consecutive years in the time series. As a particularexample, a moving event can be a Father's Day promotion that occurs ondifferent days during two consecutive years. The time series can includemagnitude spikes indicating increased interest in a product (due to theFather's Day promotion) on the different days during the two consecutiveyears.

In some examples, the processor can determine the moving events byanalyzing the time series for repetitive magnitude spikes, dips, or boththat occur with variable frequency in the time series. After determiningthe moving events, the processor can generate the base time series byremoving the magnitude spikes corresponding to the moving events.

For example, the processor can determine that a particular magnitudespike (or a particular pattern of magnitude spikes) occurs in the timeseries at varying intervals. This can cause the processor to associatethe particular magnitude spike (or the particular pattern of magnitudespikes) with a moving event. The processor can remove this magnitudespike (or the particular pattern of magnitude spikes) throughout thetime series to generate the base time series, so that the base timeseries has magnitude gaps where the magnitude spikes have been removed.

In block 1204, the processor smooths the base time series. In someexamples, the processor can smooth the base time series using anexponential smoothing method. Additionally or alternatively, theprocessor can smooth the time series by filling in the magnitude gaps inthe base time series. For example, the processor can determine anaverage magnitude value based on magnitudes of data points proximate toa magnitude gap. The processor can then fill in the magnitude gap withthe average magnitude value. The processor can repeat this process forsome or all of the magnitude gaps. In other examples, the processor canuse a predetermined magnitude value to fill in the magnitude gaps. Theprocessor can use any number and combination of techniques to fill inthe magnitude gaps.

In block 1206, the processor determines a magnitude difference betweenthe time series and the base time series. For example, the processor cansubtract the magnitude value for each point in the base time seriescorresponding to a moving event from the magnitude value for thecorresponding point in the time series. The resulting magnitudedifferences between the base time series and the time series canrepresent the effect the moving event has on the time series.

In block 1208, the processor determines if a magnitude differenceexceeds a magnitude threshold. For example, the processor can determineif the magnitude difference is statistically significant. In one suchexample, the processor can determine a standard deviation of themagnitudes of some or all of data points in the base time series. Theprocessor can use the standard deviation as the magnitude threshold.Alternatively, the processor can multiply the standard deviation by ascaling factor, such as two, and use the resulting value as themagnitude threshold. With the magnitude threshold determined, theprocessor can then determine if an absolute value of the magnitudedifference meets or exceeds the magnitude threshold.

Returning to FIG. 11, if the processor determines that the time seriesincludes a magnitude spike that exceeds a magnitude threshold, theprocessor can determine that the time series is compatible with theparticular predictive process. In some examples, the process can thenproceed to block 1116. if the processor determines that the time seriesdoes not include a magnitude spike that exceeds the magnitude threshold,the processor can determine that the time series is incompatible withthe particular predictive process.

In block 1116, the processor includes the time series in a time seriesgroup. For example, some or all of the time series determined to becompatible with the particular predictive process can be categorizedinto groups having similar magnitude patterns. A prediction of futureinterest (e.g., a forecast) can then be generated using some or all ofthe time series in a particular time series group. This can provide morerobust and accurate results than generating the prediction of futureinterest from a single time series alone. In some examples, theprocessor can include the time series in a time series group byperforming one or more steps shown in FIG. 13.

Turning to FIG. 13, in block 1302, the processor determines an attribute(or multiple attributes) of the time series. Examples of the attributecan include a frequency of an event (e.g., magnitude spike or pattern ofmagnitude spikes) in the time series, a timing of an event in the timeseries, a difference (sometimes referred to as lift and represented in apercentage) between the time series and the base time series (e.g.,determined in blocks 1202-1204), an average difference between the timeseries and the base time series, a maximum difference between the timeseries and the base time series, or any combination and derivation ofthese. For example, the processor can analyze the time series todetermine the average lift between the time series and the base timeseries.

In block 1304, the processor uses the attribute (or multiple attributes)to determine a time series group for the time series. For example, theprocessor can determine a group of time series that have similarattributes to the attribute determined in block 1302. The processor canuse a clustering method to determine the group of time series that havethe similar attributes. Examples of the clustering method can include ahierarchical clustering method, a K-means clustering method, or both ofthese. Any number and combination of clustering methods, or othergrouping methods, can be used to determine the time series group for thetime series.

In block 1306, the processor includes the time series in the time seriesgroup. The time series group can include multiple time series havingsimilar attributes, such as similar magnitude patterns duringcorresponding time periods.

Returning to FIG. 11, in some examples, blocks 1102-1120 can be repeatedfor multiple time series. By repeating blocks 1102-1120, the processorcan automatically identify multiple time series that are compatible witha particular predictive process and group the time series intocorresponding time serious groups. The processor can also automaticallyidentify time series that are incompatible with the particularpredictive process and select other, more suitable predictive-processesto use with those time series. This can allow for large-scale automationof such predictions.

In block 1122, the processor can generate a prediction of interest inthe object using the time series or the time series group with theparticular predictive process. For example, the processor can generatethe prediction of interest using one or more steps shown in FIG. 14.

FIG. 14 is a flow chart of an example of a process for predicting futureinterest in an object according to some aspects. Some examples caninclude more, fewer, or different steps than the steps depicted in FIG.14, such as steps depicted in other figures (e.g., FIGS. 11-13). Also,some examples can implement the steps of the process in a differentorder. Some examples can be implemented using any of the systems andprocesses described with respect to FIGS. 1-10.

The process shown in FIG. 14 can be conceptualized as including threestages. A first stage can include blocks 1402-1410. A second stage caninclude blocks 1412-1416. A third stage can include blocks 1418-1422.But other combinations and arrangements of the stages and blocks can beused.

In block 1402, a processor receives a time series. The time series canbe associated with an object. In some examples, the processor canreceive the time series from a local memory device. For example, theprocessor can retrieve the time series from a local memory device. Inother examples, the processor can receive the time series from a remotecomputing device via a network. For example, the processor can retrievethe time series from a remote database via the Internet. The processorcan receive some or all of the time series from any number andcombination of computing devices, databases, and memory devices.

In block 1404, the processor can process the time series. In someexamples, the processor can process the time series by implementing oneor more steps shown in FIG. 15.

Turning to FIG. 15, in block 1502, the processor aggregates the timeseries with other time series data to generate a combined time series.In some examples, the processor can determine if the time series spans aduration that is less than a threshold duration (e.g., one year). If so,the processor can aggregate the time series with the additional timeseries data to generate the combined time series. Otherwise, theprocessor may not aggregate the time series with the additional timeseries data, and may use the time series itself for the remainder of theblocks shown in FIG. 15.

In some examples, the additional time series data can include anothertime series. The other time series can be in the same time series group(e.g., the time series group determined in block 1116 of FIG. 11) as theoriginal time series (e.g., the time series received in block 1402 ofFIG. 14). For example, the processor can determine a time series groupcorresponding to the original time series. The processor can combine theoriginal time series with one or more other time series in the sametime-series group together into a single time series, which can be usedas the combined time series. In some examples, the processor can combinethe original time series with the one or more other time series using ahierarchical aggregation method.

In block 1504, the processor smooths the combined time series togenerate a smoothed time series. In some examples, the processor cansmooth the combined time series by performing one or more steps shown inblocks 1506-1510.

In block 1506, the processor removes data points from the combined timeseries that are associated with moving events, that are sporadic orinconsistent, that are outliers, or any combination of these. Forexample, the processor can receive user input or information from adatabase indicating information about a moving event. Based on theinformation, the processor can identify and remove data points in thecombined time series associated with the moving event. Removing datapoints associated with moving events, that are sporadic or inconsistent,and that are outliers can reduce the number of major magnitudevariations in the combined time series, which can make the combined timeseries easier to further analyze.

In some examples, the processor can determine that a particularmagnitude spike (or a particular pattern of magnitude spikes) occurs inthe combined time series at varying intervals. This can cause theprocessor to associate the particular magnitude spike (or the particularpattern of magnitude spikes) with a moving event. Based on the processorassociating the magnitude spike with the moving event, the processor canremove data points corresponding to the magnitude spike (or theparticular pattern of magnitude spikes) throughout the combined timeseries, leaving magnitude gaps where the magnitude spikes have beenremoved.

In blocks 1508-1510, the processor determines replacement data pointsand includes the replacement data points in the combined time series. Insome examples, the processor can determine the replacement data pointsusing an exponential smoothing method. Additionally or alternatively,the processor can determine an average magnitude value based on themagnitudes of the data points proximate to a magnitude gap in thecombined time series. The processor can then fill in the magnitude gapin the combined time series with the average magnitude value. Theprocessor can repeat this process for some or all of the magnitude gaps.In other examples, the processor can use a predetermined magnitude valueto fill in the magnitude gaps in the combined time series. The processorcan use any number and combination of techniques to fill in themagnitude gaps in the combined time series.

In block 1512, the processor uses the smoothed time series as the timeseries. For example, the processor can overwrite the time series inmemory with the smoothed time series, replace the time series in thetime series group with the smoothed time series, store the smoothed timeseries in memory, or any combination of these.

Returning to FIG. 14, in block 1406, the processor determines if thetime series exhibits a repetitive characteristic (e.g., a seasonal orperiodic characteristic). For example, the processor can analyze themagnitudes of the time series to determine if a pattern or magnitudesexists that repeats at least once during the duration of the timeseries. In some examples, the processor can determine that the timeseries includes a particular pattern of magnitudes occurring on a daily,weekly, monthly, quarterly, or yearly cycle, or with another frequency.If the processor determines that the time series does not have therepetitive characteristic, the process can continue to block 1412.Otherwise, the process can continue to block 1408.

In block 1408, the processor determines the repetitive characteristic ofthe time series. For example, the processor can use classicaltime-series decomposition or other time-series decomposition methods onthe time series to determine the repetitive characteristic of the timeseries. Time-series decomposition can be a statistical method in which atime series is deconstructed into several components, with eachcomponent representing an underlying pattern in the time series.

In block 1410, the processor generates an adjusted time series byremoving the repetitive characteristic from the time series. Forexample, the processor can generate an adjusted time series by removing,from the original time series (e.g., received in block 1402), therepetitive characteristic.

In some examples, the processor can generated a group of adjusted timeseries by removing the repetitive characteristic from some or all of thetime series in a time series group. For example, the processor canremove the repetitive characteristic from all of the time series in thetime series group in which the original time series belongs, therebygenerating a group of adjusted time series.

In blocks 1412-1414, the processor determines if the adjusted timeseries includes a moving event and, if so, determines an effect of themoving event on the adjusted time series. The effect of the moving eventcan include, for example, a magnitude spike; a magnitude dip; a patternof magnitude spikes, dips, or both; an overall increase in themagnitudes in the adjusted time series; or any combination of these. Insome examples, the processor can use a regression (e.g., linearregression) analysis or another type of statistical analysis on theadjusted time series to identify a moving event and determine the effectof the moving event on the adjusted time series.

In some examples, the processor can identify and determine the effect ofa moving event across some or all of the adjusted time series in a groupof adjusted time series. For example, the processor can perform one ormore steps shown in FIG. 16 to determine an effect of a moving event onthe group of adjusted time series.

Turning to FIG. 16, in block 1602, the processor determines a pool(e.g., a subgroup) of adjusted time series from a larger group ofadjusted time series. The larger group of adjusted time series can bethe group of adjusted time series described with respect to block 1410.The processor can determine the pool of adjusted time series using ahierarchical method, time-series pattern clustering, or both of these.For example, the processor can analyze the magnitude patterns of some orall of the adjusted time series in the larger group and assemble orcategorize the adjusted time series into pools having similar magnitudepatterns.

In block 1604, the processor determines the effects of one or moremoving events on the adjusted time series in the pool. The processor canuse a regression analysis or another type of statistical analysis on theadjusted time series in the pool to determine an overall effect of themoving event across the adjusted time series in the pool. Analyzing apool of adjusted time series can provide more accurate results thananalyzing a single adjusted time-series, particularly if the singleadjusted time-series spans a short duration or is noisy.

Returning to FIG. 14, in block 1416, the processor generates a residualtime series by removing the effect of the moving event from the adjustedtime series. A residual time series can be a form of the original timeseries that excludes the repetitive characteristic and the effect of themoving event. For example, the residual time series can be the resultingtime series after the repetitive characteristic and the effect of themoving event are removed.

In some examples, the processor can remove the effect of the movingevent from the adjusted time series by removing data points thatcorresponds to the moving event from the adjusted time series. Forexample, the processor can delete or otherwise remove some or all of thedata points in the adjusted time series that correspond to the movingevent.

In some examples, the processor can generated a group of residual timeseries by removing the effect(s) of the moving event(s) from some or allof the adjusted time series in a group of adjusted time series. Forexample, the processor can remove an effect of a moving event from allof the adjusted time series in the group of adjusted time series inwhich the original time series belongs, thereby generating a group ofresidual time series. As another example, the processor can remove aneffect of a moving event from all of the adjusted time series in a poolof adjusted time series (e.g., the pool determined in block 1602 of FIG.16), thereby generating a group of residual time series.

In block 1418, the processor generates a base model (e.g., a baseforecast) using the residual time series. In some examples, theprocessor can generate the base model using univariate time-seriesmodeling techniques. For example, the processor can generate the basemodel using an ARIMA model, an ARIMAX model, or an ESM.

The base model can represent interest in the object over a future periodof time, such as one year. The duration of the future time period can becustomizable by a user. For example, the processor can receive inputindicating that the base model is to predict interest in the object overa longer future period of time, such as two years, and generate a basemodel that predicts the interest over that longer future period of time.

In some examples, the processor can use the base model as a predictivemodel to predict interest in the object over the future period of timefor which the base model was generated. But the prediction can be moreaccurate if the processor includes the effect of the moving event, therepetitive characteristic, or both of these into the base model, asdiscussed below.

In block 1420, the processor includes the effect of the moving event(e.g., determined in block 1414) into the base model. For example, theprocessor can determine that a magnitude spike associated with Father'sDay is to be included at a location in the base model corresponding tothe date of Father's Day (or at multiple locations in the base modelcorresponding to the dates of Father's Day if the base model spansseveral years). The processor can then include the magnitude spike inthe base model at the location. For example, the processor can increasethe magnitude of a data point in the base model that corresponds to thedate of Father's Day.

In some examples, the processor can include multiple effects associatedwith multiple moving events into the base model. For example, theprocessor can include the effects of promotions, company events, Easter,Father's Day, and Mother's Day into the base model. Including theeffect(s) of one or more moving events in the base model can increasethe accuracy of the base model.

In block 1422, the processor can include the repetitive characteristicinto the base model. For example, the processor can include magnitudespikes or dips associated with the repetitive characteristic (e.g.,determined in block 1408) into the base model. In one particularexample, the processor can change the magnitudes of data pointscorresponding to dates associated with the repetitive characteristic tocapture the effect of the repetitive characteristic. Including theeffect(s) of the repetitive characteristic in the base model canincrease the accuracy of the base model.

In some examples, after including the effect of the moving event, therepetitive characteristic, or both of these into the base model, theresult can be a forecast that can provide an accurate prediction ofinterest in the object over a future period of time.

Example Implementation of the Three-Stage Process

One example of the process described with respect to FIG. 14 beingimplemented is shown in FIGS. 17-25. FIG. 17 is a graph 1700 of anexample of a time series 1710 according to some aspects. The time series1710 represents sales data. The X-axis of the graph 1700 shows atimeframe between the years 2011 and 2017. The right Y-axis indicates anumber of sales of a product (in units) during the timeframe. The leftY-axis indicates when moving events occur. A value of one indicates thata moving event occurred. Also shown on graph 1700 are various movingevents that occur during the timeframe. For example, spike 1702 isassociated with Father's Day. Spike 1708 is associated with Christmas.Spikes 1704, 1706 are associated with other holidays. Although all thesemoving events occur periodically throughout the timeframe (asrepresented by the periodic spikes), the moving events occur ondifferent days of the week each year (e.g., Saturday in 2011, Sunday in2012, Tuesday in 2013, etc.), which is why they are considered movingevents.

FIG. 18 is a graph 1800 of an example of the decomposition of the timeseries 1710 from FIG. 17 after smoothing the time series according tosome aspects. The graph 1800 shows the time series 1710, a seasonalcomponent (e.g., repetitive characteristic) of the time series 1806, anda trend component 1804 of the time series. The trend component 1804 canindicate the long term trend or pattern in the time series over thetimeframe. In this example, the trend component 1804 shows a long termdecrease in sales over the timeframe.

FIG. 19 is a graph 1900 of an example of the time series 1710 from FIG.17 against a seasonally adjusted time series 1904 (e.g., a time seriesfor which the repetitive characteristic has been removed) according tosome aspects. As shown, the seasonally adjusted sales data has fewerdrastic spikes and dips than the original sales data 1902. In someexamples, the seasonally adjusted sales data can be the output fromblock 1410 of FIG. 14.

FIG. 20 is a graph 2000 of an example of the time series 1710 from FIG.17 against the estimated effect of promotions (e.g., the effects ofmoving events) on the time series according to some aspects. FIG. 21 isa graph 2100 of an example of the time series 1710 from FIG. 17 againsta residual time series 2104 after the effects of the promotions havebeen removed according to some aspects. In some examples, the residualtime series 2104 can be the output from block 1416 of FIG. 14.

FIG. 22 is a graph 2200 of an example of a final forecast according tosome aspects. The final forecast can be generated by performing thesteps shown in blocks 1418-1422 of FIG. 14. The points 2206 to the leftof the bar 2208 show the original time-series data. Line 2202 can be abase model (e.g., generated in block 1418 of FIG. 14). Line 2204 can bea final model (e.g., a final forecast generated in block 1422 of FIG.14). The shading surrounding the line 2204 can represent the statisticalconfidence limits for the final model. As shown, the final model canaccurately predict sales through the year 2015.

Predictions generated using the three-stage process shown in FIG. 14 canbe more accurate than predictions generated using other processes. Forexample, FIG. 23 is a graph 2300 of an example of actual sales againstpredicted sales generated using the three-stage process according tosome aspects. Line 2308 shows actual sales during the year 2014, line2310 shows actual sales during the year 2015, and line 2312 showspredicted sales during the year 2016. As shown, lines 2308 and 2310include a magnitude spike around fiscal week 19. Line 2312 includes amagnitude spike around fiscal week 20. These magnitude spikes cancorrespond to a spike in sales during the week before Father's Day.Because Father's Day shifted from fiscal week 19 in the years 2014-2015to fiscal week 20 in the year 2016, the predicted sales shown by line2312 accurately reflect this shift.

Conversely, other processes may fail to capture moving events orotherwise may be less accurate. For example, FIG. 24 is a graph 2400that includes line 2402 showing actual sales during the year 2014, line2404 showing actual sales during the year 2015, and line 2406 showingpredicted sales during the year 2016. The predicted sales were generatedusing an ESM model. As shown by the alignment of peaks 2408, thepredicted sales during the year 2016 align with the actual sales fromthe previous years, failing take into account the shift for Father's Dayfrom fiscal week 19 to fiscal week 20.

As another example, FIG. 25 shows a graph 2500 that includes a line 2502showing actual sales during the year 2014, line 2504 showing actualsales during the year 2015, and line 2406 showing predicted sales duringthe year 2016. The predicted sales were generated using an ARIMAX model.As shown, predicted sales during the year 2016 align with the actualsales from the previous years, failing take into account the shift forFather's Day from fiscal week 19 to fiscal week 20.

The foregoing description of certain examples, including illustratedexamples, has been presented only for the purpose of illustration anddescription and is not intended to be exhaustive or to limit thedisclosure to the precise forms disclosed. Numerous modifications,adaptations, and uses thereof will be apparent to those skilled in theart without departing from the scope of the disclosure.

1. A non-transitory computer readable medium comprising program codeexecutable by a processor for causing the processor to: receive aplurality of time series, each time series of the plurality of timeseries comprising a plurality of data points arranged in a sequentialorder over a period of time; filter the plurality of time series using apreset time duration to identify a subset of time series that have timedurations that exceed the preset time duration, the preset time durationbeing a minimum time duration usable with a preselected forecastingprocess; and in response to identifying the subset of time series thatexceeds the preset time duration: determine that a time series of thesubset of time series does not include a time period with inactivity;determine that the time series exhibits a repetitive characteristicbased on the time series comprising a pattern that repeats over apredetermined time period; determine that the time series comprises amagnitude spike with a value above a preset magnitude threshold; and inresponse to determining that the time series (i) lacks the time periodwith inactivity, (ii) exhibits the repetitive characteristic, and (iii)comprises the magnitude spike with the value above the preset magnitudethreshold: generate a data set that includes the time series; andgenerate a predictive forecast from the data set using the preselectedforecasting process, the predictive forecast indicating a progression ofthe time series over a future period of time.
 2. The non-transitorycomputer readable medium of claim 1, wherein the preselected forecastingprocess comprises: determining the repetitive characteristic exhibitedby the time series; generating an adjusted time series by removing therepetitive characteristic from the time series; determining, using theadjusted time series, an effect of one or more moving events that occuron different dates for two or more consecutive years on the adjustedtime series; generating a residual time series by removing the effect ofthe one or more moving events from the adjusted time series; generating,using the residual time series, a base forecast that is independent ofthe repetitive characteristic and the effect of the one or more movingevents; and generating the predictive forecast by including therepetitive characteristic and the effect of the one or more movingevents into the base forecast.
 3. The non-transitory computer readablemedium of claim 1, further comprising program code executable by theprocessor for causing the processor to determine that the time seriescomprises the magnitude spike with the value above the preset magnitudethreshold by: removing the repetitive characteristic from the timeseries to generate a base time series; determining one or more magnitudedifferences between the time series and the base time series;determining that the one or more magnitude differences exceed the presetmagnitude threshold; and in response to determining that the one or moremagnitude differences exceed the preset magnitude threshold, determiningthat the time series comprises the magnitude spike with the value abovethe preset magnitude threshold.
 4. The non-transitory computer readablemedium of claim 1, further comprising program code executable by theprocessor for causing the processor to generate the data set thatincludes the time series by: determining a time-series group for thetime series from a plurality of time-series groups using a clusteringmethod; and including the time series in the time-series group, thetime-series group being the data set.
 5. The non-transitory computerreadable medium of claim 4, further comprising program code executableby the processor for causing the processor to: determine the time-seriesgroup for the time series from the plurality of time-series groups usingthe clustering method by: determining an attribute of the time seriescomprising a frequency of events in the time series, a timing of eventsin the time series, an average percentage of lift with respect to a basetime series, or a maximum percentage of lift with respect to the basetime series; using the attribute of the time series as input for theclustering method; and receiving the time-series group as output fromthe clustering method.
 6. The non-transitory computer readable medium ofclaim 1, wherein the time series is a first time series, and furthercomprising program code executable by the processor for causing theprocessor to: determine that a time duration of a second time series ofthe plurality of time series is below the preset time duration usablewith the preselected forecasting process; or determine that the secondtime series comprises the time period with the inactivity; or determinethat the second time series does not exhibit the repetitivecharacteristic based on an absence of the event; or determine that thesecond time series does not comprise the magnitude spike with the valueabove the preset magnitude threshold; and in response to determiningthat (i) the time duration of the second time series is below the presettime duration, (ii) the second time series comprises the time periodwith the inactivity, (iii) the second time series does not exhibit therepetitive characteristic, or (iii) the second time series does notcomprise the magnitude spike with the value above the preset magnitudethreshold, flag the second time series as incompatible with thepreselected forecasting process.
 7. The non-transitory computer readablemedium of claim 6, further comprising program code executable by theprocessor for causing the processor to: select another forecastingprocess for use with the second time series; and use the otherforecasting process to generate another forecast from the second timeseries.
 8. The non-transitory computer readable medium of claim 1,wherein the preset time duration usable with the preselected forecastingprocess is a first preset time duration, and further comprising programcode executable by the processor for causing the processor to: prior todetermining the time series exhibits the repetitive characteristic,determine that a time duration of the time series is above the firstpreset time duration and below a second preset time duration and, inresponse: aggregate the time series with another time series to generatean aggregate time series; and use the aggregate time series as the timeseries.
 9. The non-transitory computer readable medium of claim 8,wherein the first preset time duration is one year and the second presettime duration is two years.
 10. The non-transitory computer readablemedium of claim 1, wherein the non-transitory computer readable mediumcomprises two or more computer readable media distributed among two ormore worker nodes in a communications grid computing system, the two ormore worker nodes being separate computing devices that are remote fromone another.
 11. A method comprising: receiving a plurality of timeseries, each time series of the plurality of time series comprising aplurality of data points arranged in a sequential order over a period oftime; filtering the plurality of time series using a preset timeduration to identify a subset of time series that have time durationsthat exceed the preset time duration, the preset time duration being aminimum time duration usable with a preselected forecasting process; andin response to identifying the subset of time series that exceeds thepreset time duration: determining that a time series of the subset oftime series does not include a time period with inactivity; determiningthat the time series exhibits a repetitive characteristic based on thetime series comprising a pattern that repeats over a predetermined timeperiod; determining that the time series comprises a magnitude spikewith a value above a preset magnitude threshold; and in response todetermining that the time series (i) lacks the time period withinactivity, (ii) exhibits the repetitive characteristic, and (iii)comprises the magnitude spike with the value above the preset magnitudethreshold: generating a data set that includes the time series; andgenerating a predictive forecast from the data set using the preselectedforecasting process, the predictive forecast indicating a progression ofthe time series over a future period of time.
 12. The method of claim11, wherein the preselected forecasting process comprises: determiningthe repetitive characteristic exhibited by the time series; generatingan adjusted time series by removing the repetitive characteristic fromthe time series; determining, using the adjusted time series, an effectof one or more moving events that occur on different dates for two ormore consecutive years on the adjusted time series; generating aresidual time series by removing the effect of the one or more movingevents from the adjusted time series; generating, using the residualtime series, a base forecast that is independent of the repetitivecharacteristic and the effect of the one or more moving events; andgenerating the predictive forecast by including the repetitivecharacteristic and the effect of the one or more moving events into thebase forecast.
 13. The method of claim 11, further comprisingdetermining that the time series comprises the magnitude spike with thevalue above the preset magnitude threshold by: removing the repetitivecharacteristic from the time series to generate a base time series;determining one or more magnitude differences between the time seriesand the base time series; determining that the one or more magnitudedifferences exceed the preset magnitude threshold; and in response todetermining that the one or more magnitude differences exceed the presetmagnitude threshold, determining that the time series comprises themagnitude spike with the value above the preset magnitude threshold. 14.The method of claim 11, further comprising generating the data set thatincludes the time series by: determining a time-series group for thetime series from a plurality of time-series groups using a clusteringmethod; and including the time series in the time-series group, thetime-series group being the data set.
 15. The method of claim 14,further comprising determining the time-series group for the time seriesfrom the plurality of time-series groups using the clustering method by:determining an attribute of the time series comprising a frequency ofevents in the time series, a timing of events in the time series, anaverage percentage of lift with respect to a base time series, or amaximum percentage of lift with respect to the base time series; usingthe attribute of the time series as input for the clustering method; andreceiving the time-series group as output from the clustering method.16. The method of claim 11, wherein the time series is a first timeseries, and further comprising: determining that a time duration of asecond time series of the plurality of time series is below the presettime duration usable with the preselected forecasting process; ordetermining that the second time series comprises the time period withthe inactivity; or determining that the second time series does notexhibit the repetitive characteristic based on an absence of the event;or determining that the second time series does not comprise themagnitude spike with the value above the preset magnitude threshold; andin response to determining that (i) the time duration of the second timeseries is below the preset time duration, (ii) the second time seriescomprises the time period with the inactivity, (iii) the second timeseries does not exhibit the repetitive characteristic, or (iii) thesecond time series does not comprise the magnitude spike with the valueabove the preset magnitude threshold, flagging the second time series asincompatible with the preselected forecasting process.
 17. The method ofclaim 16, further comprising: selecting another forecasting process foruse with the second time series; and using the other forecasting processto generate another forecast from the second time series.
 18. The methodof claim 11, wherein the preset time duration usable with thepreselected forecasting process is a first preset time duration, andfurther comprising prior to determining the time series exhibits therepetitive characteristic, determining that a time duration of the timeseries is above the first preset time duration and below a second presettime duration and, in response: aggregating the time series with anothertime series to generate an aggregate time series; and using theaggregate time series as the time series.
 19. The method of claim 18,wherein the first preset time duration is one year and the second presettime duration is two years.
 20. The method of claim 11, wherein:generating the data set comprises a first worker node of acommunications grid computing system receiving information from a secondworker node of the communications grid computing system, generating thedata set based on the information, and transmitting the data set to athird worker node of the communications grid computing system; andgenerating the predictive forecast comprises the third worker node ofthe communications grid computing system receiving the data set andgenerating the predictive forecast based on the data set.
 21. A systemcomprising: a processing device; and a memory device in whichinstructions executable by the processing device are stored for causingthe processing device to: receive a plurality of time series, each timeseries of the plurality of time series comprising a plurality of datapoints arranged in a sequential order over a period of time; filter theplurality of time series using a preset time duration to identify asubset of time series that have time durations that exceed the presettime duration, the preset time duration being a minimum length usablewith a preselected forecasting process; and in response to identifyingthe subset of time series that exceeds the preset time duration:determine that a time series of the subset of time series does notinclude a time period with inactivity; determine that the time seriesexhibits a repetitive characteristic based on the time series comprisinga pattern that repeats over a predetermined time period; determine thatthe time series comprises a magnitude spike with a value above a presetmagnitude threshold; and in response to determining that the time series(i) lacks the time period with inactivity, (ii) exhibits the repetitivecharacteristic, and (iii) comprises the magnitude spike with the valueabove the preset magnitude threshold: generate a data set that includesthe time series; and generate a predictive forecast from the data setusing the preselected forecasting process, the predictive forecastindicating a progression of the time series over a future period oftime.
 22. The system of claim 21, wherein the memory device furthercomprises instructions executable by the processing device for causingthe processing device to generate the predictive forecast by:determining the repetitive characteristic exhibited by the time series;generating an adjusted time series by removing the repetitivecharacteristic from the time series; determining, using the adjustedtime series, an effect of one or more moving events that occur ondifferent dates for two or more consecutive years on the adjusted timeseries; generating a residual time series by removing the effect of theone or more moving events from the adjusted time series; generating,using the residual time series, a base forecast that is independent ofthe repetitive characteristic and the effect of the one or more movingevents; and generating the predictive forecast by including therepetitive characteristic and the effect of the one or more movingevents into the base forecast.
 23. The system of claim 21, wherein thememory device further comprises instructions executable by theprocessing device for causing the processing device to: determine thatthe time series comprises the magnitude spike with the value above thepreset magnitude threshold by: removing the repetitive characteristicfrom the time series to generate a base time series; determining one ormore magnitude differences between the time series and the base timeseries; determining that the one or more magnitude differences exceedthe preset magnitude threshold; and in response to determining that theone or more magnitude differences exceed the preset magnitude threshold,determining that the time series comprises the magnitude spike with thevalue above the preset magnitude threshold.
 24. The system of claim 21,wherein the memory device further comprises instructions executable bythe processing device for causing the processing device to generate thedata set that includes the time series by: determining a time-seriesgroup for the time series from a plurality of time-series groups using aclustering method; and including the time series in the time-seriesgroup, the time-series group being the data set.
 25. The system of claim24, wherein the memory device further comprises instructions executableby the processing device for causing the processing device to: determinethe time-series group for the time series from the plurality oftime-series groups using the clustering method by: determining anattribute of the time series comprising a frequency of events in thetime series, a timing of events in the time series, an averagepercentage of lift with respect to a base time series, or a maximumpercentage of lift with respect to the base time series; using theattribute of the time series as input for the clustering method; andreceiving the time-series group as output from the clustering method.26. The system of claim 21, wherein the time series is a first timeseries, and wherein the memory device further comprises instructionsexecutable by the processing device for causing the processing deviceto: determine that a time duration of a second time series of theplurality of time series is below the preset time duration usable withthe preselected forecasting process; or determine that the second timeseries comprises the time period with the inactivity; or determine thatthe second time series does not exhibit the repetitive characteristicbased on an absence of the event; or determine that the second timeseries does not comprise the magnitude spike with the value above thepreset magnitude threshold; and in response to determining that (i) thetime duration of the second time series is below the preset timeduration, (ii) the second time series comprises the time period with theinactivity, (iii) the second time series does not exhibit the repetitivecharacteristic, or (iii) the second time series does not comprise themagnitude spike with the value above the preset magnitude threshold,flag the second time series as incompatible with the preselectedforecasting process.
 27. The system of claim 26, wherein the memorydevice further comprises instructions executable by the processingdevice for causing the processing device to: select another forecastingprocess for use with the second time series; and use the otherforecasting process to generate another forecast from the second timeseries.
 28. The system of claim 21, wherein the preset time durationusable with the preselected forecasting process is a first preset timeduration, and wherein the memory device further comprises instructionsexecutable by the processing device for causing the processing deviceto: prior to determining the time series exhibits the repetitivecharacteristic, determine that a time duration of the time series isabove the first preset time duration and below a second preset timeduration and, in response: aggregate the time series with another timeseries to generate an aggregate time series; and use the aggregate timeseries as the time series.
 29. The system of claim 28, wherein the firstpreset time duration is one year and the second preset time duration istwo years.
 30. The system of claim 21, further comprising a pluralityworker nodes in a communications grid computing system, wherein: a firstworker node of the plurality of worker nodes is configured to generatethe data set and transmit the data set to a second worker node of theplurality of worker nodes; and the second worker node of the pluralityof worker nodes is configured to receive the data set and generate thepredictive forecast based on the data set.